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Journal ArticleDOI

Real-Time Regularized Ultrasound Elastography

TLDR
This paper introduces two real-time elastography techniques based on analytic minimization (AM) of regularized cost functions that produce axial strain and integer lateral displacement, while the second method produces both axial and lateral strains.
Abstract
This paper introduces two real-time elastography techniques based on analytic minimization (AM) of regularized cost functions. The first method (1D AM) produces axial strain and integer lateral displacement, while the second method (2D AM) produces both axial and lateral strains. The cost functions incorporate similarity of radio-frequency (RF) data intensity and displacement continuity, making both AM methods robust to small decorrelations present throughout the image. We also exploit techniques from robust statistics to make the methods resistant to large local decorrelations. We further introduce Kalman filtering for calculating the strain field from the displacement field given by the AM methods. Simulation and phantom experiments show that both methods generate strain images with high SNR, CNR and resolution. Both methods work for strains as high as 10% and run in real-time. We also present in vivo patient trials of ablation monitoring. An implementation of the 2D AM method as well as phantom and clinical RF-data can be downloaded.

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Citations
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Journal ArticleDOI

Global Time-Delay Estimation in Ultrasound Elastography

TL;DR: A novel technique for time-delay estimation (TDE) of all samples of RF data simultaneously, thereby exploiting all the information in RF data for TDE, and is solved in real time using a computationally efficient optimization technique.
Journal ArticleDOI

Self-similarity weighted mutual information: a new nonrigid image registration metric.

TL;DR: This work proposes a self-similarity weighted graph-based implementation of α-mutual information (α-MI) for nonrigid image registration and shows that SeSaMI produces a robust and smooth cost function and outperforms the state of the art statistical based similarity metrics in simulation and using data from image-guided neurosurgery.
Journal ArticleDOI

Automatic Deformable MR-Ultrasound Registration for Image-Guided Neurosurgery

TL;DR: This work presents a novel algorithm for registration of 3-D volumetric ultrasound (US) and MR using Robust PaTch-based cOrrelation Ratio (RaPTOR), which is invariant to large amounts of spatial intensity inhomogeneity and proposes a novel outlier suppression technique based on the orientations of the RaPTOR gradients.
Journal ArticleDOI

Learning the implicit strain reconstruction in ultrasound elastography using privileged information

TL;DR: This work is the first to develop an implicit strain reconstruction framework by a deep neural network architecture that proposes the learning-using-privileged-information (LUPI) paradigm with causality in the network to correct the intermediate state of the learning process.
Book ChapterDOI

Self-similarity weighted mutual information: A new nonrigid image registration metric

TL;DR: This work proposes a self-similarity weighted graph-based implementation of alpha-mutual information (alpha-MI) for nonrigid image registration that produces a robust and smooth cost function and outperforms the state of the art statistical based similarity metrics in simulation and using data from image-guided neurosurgery.
References
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Book

Biomechanics: Mechanical Properties of Living Tissues

TL;DR: This chapter discusses the mechanics of Erythrocytes, Leukocytes, and Other Cells, and their role in Bone and Cartilage, and the properties of Bioviscoelastic Fluids, which are a by-product of these cells.
Journal ArticleDOI

Biomechanics: Mechanical Properties of Living Tissues

TL;DR: In this article, the authors present a sketch of the history and scope of the field of bio-physiology and discuss the meaning of the Constitutive Equation and the flow properties of blood.
Proceedings Article

An introduction to the Kalman filter

G. Welch
Journal ArticleDOI

Lucas-Kanade 20 Years On: A Unifying Framework

TL;DR: In this paper, a wide variety of extensions have been made to the original formulation of the Lucas-Kanade algorithm and their extensions can be used with the inverse compositional algorithm without any significant loss of efficiency.
BookDOI

An Introduction to the Kalman Filter

TL;DR: The discrete Kalman filter as mentioned in this paper is a set of mathematical equations that provides an efficient computational (recursive) means to estimate the state of a process, in a way that minimizes the mean of the squared error.
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